Model
Select capability according to the task — reasoning, language, vision, speed, cost, privacy and deployment constraints.
AI
Enterprise AI is more than a model. Useful systems combine models with the right context, information, tools, workflows and human judgment.
The enterprise AI stack
Select capability according to the task — reasoning, language, vision, speed, cost, privacy and deployment constraints.
Give the system the information it needs: documents, policies, history, location, date, user role and current data.
Connect search, databases, APIs, applications and approved actions when answering is not enough.
Place AI inside a workflow with clear inputs, outputs, exceptions, controls and escalation paths.
Decide where people ask, review, approve, correct, teach and take responsibility.
Keep enough information to understand sources, versions, decisions, actions and outcomes.
Context
Training data, a prompt, a web search, enterprise documents and conversation memory are not the same thing. Each contributes different information, freshness and risk.
Good AI design starts by deciding what information should be available for a specific task — and what should not.
The right question is not only “Which model?” It is also “What should this system know right now?”
Where AI can help
Search, summarize and answer questions using enterprise information with sources.
Classify, extract, compare, review and transform information from documents and messages.
Support analysis, prioritization and recommendations while keeping appropriate human oversight.
Coordinate tools and steps so AI can perform bounded tasks instead of only producing text.
Combine text, images, audio and structured data when the task crosses formats.
Test quality, latency, cost and risk before selecting a model or architecture.
Related insight
Context determines whether a capable model has the information needed for the task. We separate model knowledge, enterprise knowledge, web information, memory and real-time data.